Method and system for identifying subrogation potential and valuing a subrogation file

ABSTRACT

A method for identifying select ones of insurance records which possess a favorable subrogation potential. The method includes receiving data indicative of a plurality of claims; automatically calculating a base score to identify select ones of the claims which demonstrate at least a given probability of expected subrogation recovery dependently upon the received data; automatically identifying risk factors for each of the select claims; and, automatically scoring each of the select claims dependently upon the base scores and identified risk factors to provide a value indicative of an expected subrogation recovery.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.09/676,391, filed Sep. 29, 2000, now U.S. Pat. No. 7,343,308, which inturn claims priority benefit of and is related to commonly assigned andcopending U.S. Patent application Ser. No. 60/207,246, entitled “ONLINEMETHOD AND SYSTEM FOR FULFILLING NEEDS RESULTING FROM PROPERTY AND OTHERSIMILAR LOSSES” filed May 26, 2000, all of which are hereby incorporatedby reference in their entireties for all purposes.

FIELD OF THE INVENTION

The present invention relates generally to subrogation and moreparticularly to methods and systems for identifying subrogationpotential and subrogation file valuing.

BACKGROUND OF THE INVENTION

Generally, subrogation refers to an insurer's right to recover lossespaid under insurance contracts from parties legally liable for thedamages. A problem often encountered is that an insurer or entity thatis designated to recover the loss can incur significant costs inassessing the collect-ability of the claimed recovery. Insurers alsobear the financial and fiduciary risk of failing to recognize thatspecific claims may have subrogation recovery potential.

In the insurance industry as well as other financial institutions suchas banking, scoring has long been an integral part of underwritingoperations. This is largely made possible because of productstandardization and buyer homogeneity.

To reduce costs, it has been found desirable to provide an expertrating, or scoring method which identifies subrogation potential earlyon in a claims handling process, and serves as a predictor ofrecoverability in order to improve collection efficiencies. It is afurther object of the present invention to enable pooling or packagingof claims into portfolios for eventual sale of the subrogation rights onthe underlying claims to entities that specialize in such collectionactivities, for example.

SUMMARY OF THE INVENTION

A method for identifying select ones of insurance records which possessa favorable subrogation potential, the method including: receiving dataindicative of a plurality of claims; automatically calculating a basescore to identify select ones of the claims which demonstrate at least agiven probability of expected subrogation recovery dependently upon thereceived data; automatically identifying risk factors for each of theselect claims; and, automatically scoring each of the select claimsdependently upon the base scores and identified risk factors to providea value indicative of an expected subrogation recovery.

BRIEF DESCRIPTION OF THE FIGURES

Various other objects, features and advantages of the invention willbecome more apparent by reading the following detailed description inconjunction with the drawings, which are shown by way of example only,wherein:

FIG. 1 illustrates a block diagram of a first method for practicing thepresent invention;

FIG. 2 illustrates a block diagram of a second method for practicing thepresent invention;

FIG. 3 is a block diagram, which illustrates how subrogationopportunities are identified according to the invention;

FIG. 4 is a block diagram, which illustrates the use of the datawarehouse in subrogation scoring according to the invention; and

FIG. 5 is a block diagram, which illustrates how the invention compiles,values and sells subrogation rights.

DETAILED DESCRIPTION OF THE INVENTION

Subrogation identification refers to a method for recognizing if a claimfile has subrogation potential or might be found to have potential afterfurther investigation Subrogation scoring refers to a method for valuinga subrogation file. The score provides a measure of the collectabilityof a subrogation claim. Once a score of collectability is determined, amonetary value can be associated with it. This enables the subrogationrights on groups of claims to be bundled into a marketable “security”.These securities may then be sold to entities possessing collectioncapabilities, for example.

In other words, the existence of a centralized collection of claimswould allow subrogation claims files to be created. Once the value ofsuch files are established, a market for the sale of their subrogationrights may be established. Essentially an exchange of marketablesubrogation claims could be created. In a business-to-business (B2B)exchange, sellers, market makers and investors can transact forwholesale claims, pre- and post-accident purchase and sale of traunchesof risk obligations and subrogation rights.

The present system and method preferably automatically scores a claim todetermine a likelihood of subrogation potential, and then, the value ofthe claim recovery. A checklist is provided for the recovery specialistto follow to standardize operations and maximize recoveries. Thesubrogation opportunity is valued by reviewing criteria such as accidentdescription, loss state, responsible party, and other pertinentdemographics. Once the system and method assigns a subrogationidentification rating, a score and valuation, the claims with definitesubrogation potential are bundled or pooled with other claims that havesome commonality to the newest claim. Thereafter, the bundled set orportfolio is valued as to its sale price. As a central market forwholesale claim service and financial liquidity, the invention canutilize network economics to further reduce claim handling costs to itsparticipating insurance companies, while building an electronic databaseabout claims handling.

Referring now to FIG. 3, in one embodiment of the present inventionsubrogation opportunities are identified using claim data 70 obtainedvia a web site 72 during an interview or interrogation process 74, forexample. The claim data is analyzed 76 for state, type of claim,accident description, loss date, claim notes, presence of a favorablepolice report, existence of an insurance carrier, and other similarcriteria. As subrogation opportunities are identified 78, the claimant'sfile is passed to the subrogation process for servicing. If nosubrogation opportunity is found, the file is closed 79.

Factor values are derived from an assessment of similar historicalclaims recoveries. Using these factors, a probability of recovery iscalculated. The factors and weights potentially vary by customer. Thesystem assigns a strategy and checklist for working the account based onthe score resulting from the calculated probability of recovery as wellas on the type and circumstances of the claim. The scoring is thenapplied to a grouping of claims/accounts to determine the aggregatevalue of the group. The value of the group is used in a subrogationclaim exchange process 86.

FIG. 5 illustrates how the invention compiles, values and sellssubrogation rights. Claims files 90 stored in the data warehouse 82 aresearched to identify 91 claims with subrogation rights and potential. Aclaim with subrogation potential is scored 92 as to the probability ofits recovery, and the cost of recovering the claim is estimated 93. Therecovery score and the estimated timing and costs of recovery assessmentare used to set a value 94 for the claim. Once the claim is valued, itis bundled or pooled 95 into a group with other claims that have somecommonality to the claim. In particular, the claims are groupedaccording to pre-established criteria, examples of which include but arenot limited to dollar value, type of claims (automobile, homeowners)state or region. The bundled group or portfolio of claims is then valued96 as to its sale price and risk potential and offered for sale 97 in anauction-like manner. The bid, ask and sale prices are then recorded 98in the data warehouse.

Referring again to the subrogation scoring 92, it predicts the potentialfor claim loss and deductible recoverability. Insurers and subrogationcollection agencies can use this score to optimize collection strategiesand staffing levels, price assignments of subrogation rights and setnetback: guarantee rates for example.

A subrogation score can be used to set up strategies for contingentcollection services, further reducing internal costs. It can be used toestablish manpower needs, set goals, and measure collector efficiencies.In an environment where the subrogation rights are grouped intocategories based upon multiple criteria, one of which is a valuationbased upon a subrogation score, these groups or portfolios can beoffered for sale. In this case the score would be integral to pricingthe portfolio and evaluating risk.

Three types of subrogation scores that predict these recoveries,including or excluding collection expense utilized according to apreferred form of the present invention are: COLLECTION SCORE, NET BACKSCORE and NET LIQUIDATION SCORE. All scores reflect predictions ofsubrogation recoveries for individual claims. All scores are derivedfrom algorithms factoring in characteristics of: underlying insurance,the underlying insurance claim, legally liable party (tortfeasor), thetortfeasor's insurance carrier, the current economic environment, andcollection operations, for example.

The COLLECTION SCORE focuses on the expected ultimate loss recovery. TheNETBACK SCORE is a composite scoring valuing recoveries net ofcollection expense. The NET LIQUIDATION SCORE is a more sophisticatedversion of the NETBACK SCORE. Values reflect the net present value ofultimate losses recovered and costs expensed. It should be recognizedthat this is reflective of a future market in subrogation portfoliosales.

Basically, the NETBACK SCORE=LIQUIDATION SCORE−EXPENSE SCORE. TheLIQUIDATION SCORE component expands the COLLECTION SCORE to incorporatethe element of time. A set of LIQUIDATION SCORES is created reflectingrates of recoveries at 12, 15, 18, or 24 months as well as at ultimate,for example. The EXPENSE SCORE factors in unit cost estimates based onoperational strategies. These can be generalized for common consumptionor customized for specific:buyers.

Referring now to FIG. 1., therein is illustrated a preferred form of thepresent invention 100, wherein data for a claim is received through oneof several methods such as extraction from a web-based interface at thepoint of loss entry, file transfer of batch of claims closed during atarget period, or batch of claims just placed for subrogation collection110. A base score, or COLLECTION SCORE, is calculated 120, a set ofexternal databases is queried for additional data relating to the claim,such as the legally liable party or the party's insurance carriers 130,risk factors are identified 140 and a final score, or value, indicativeof the expected recovery is provided 150.

According to a preferred form, the COLLECTION SCORE, related to theexpected recovery, rate is first calculated for each claim by: (1)calculating an expected probability a legally liable party will make apayment; (2) calculating an expected probable percentage of lossesrecovered through payments received from legally liable parties; (3)adjusting resultant scores for differences due to economic conditions oroperation strategies or efficiencies; and, (4) identifying specific riskfactors associated with the individual claims and adjusting theresultant score accordingly.

Next, the NETBACK SCORE is calculated for each of the claims by: (1)calculating the LIQUIDATION SCORE at specific periods of time; and, (2)calculating the EXPENSE SCORE for each of the specified periods of time.Next, the NET LIQUIDATION SCORE is calculated by analogously.

Referring now also to FIG. 2, therein is illustrated an alternativemethod equally applicable to the present invention, wherein likeelements or steps to those of FIG. 1 are designated with a ' symbol.Basically, data for a claim is received 110′, a base score, orCOLLECTION SCORE, is calculated 120′ by querying an external databasefor example, a risk factor is identified 140′ and a final score, orvalue, indicative of the expected recovery is provided 150′. If morerisk factors remain to be considered, steps 140′ and 150′ are repeated160′.

The present invention's method of subrogation collection scoring focuseson the expected ultimate loss recoverability, as well as the collectiontimeframe. Referring now more particularly to variables considered whichare used to derive these scores using appropriate algorithms, for theunderlying insurance, characteristic variables used preferably include:whether the claim arises from a preferred, standard, nonstandard oraffinity policy group; whether a personal or commercial lines policy isinvolved; and, whether the responsible party has insurance as well as ifboth first and third party insurers are members of a common alternativedispute resolution organization.

Variables associated with the underlying insurance claim preferablyinclude: the number of subrogation collection agencies who havepreviously worked the account; the type of underlying claim; the size ofthe claim; the length of time since loss occurrence when the presentcollection effort is initiated; the negligence laws associated with thestate of legal jurisdiction; the relative degree of insured's andtortfeasor's negligence; whether legal action was initiated understatute of limitation or whether there was some extension of statute oflimitations; and, whether there exists any type of legal judgmentsrendered.

The variables used associated with the legally liable party (tortfeasor)preferably include: for uninsured and underinsured individuals or groupsof individuals-income levels; homeownership; gender; presence ofchildren; number of years at current residence; age; and, maritalstatus. For uninsured businesses, variables used preferably include:bankruptcy; existence of liens; judgments; derogatory legal information;problems in paying suppliers; number of employees (size of business);other financial strength indicators; length of time in business;business structure (incorporated, “doing business as”, LLC); and type ofbusiness. For other uninsured parties, the variables preferably usedinclude: whether the party is a nonprofit agency; fraternal in nature;or, a type of government agency.

For the tortfeasor's insurer, variables used preferably include:financial strength; resistance to pay characteristics; common membershipin alternative resolution pools; and, the type of insurance business.

For the current economic environment characteristic, variables usedpreferably include: inflation; interest rates and tight moneyconditions; unemployment and bankruptcy levels; financial strength oflegally liable party's industry and geographic region; and generalizedor unique subrogation operation expense and effectiveness assumptions.

Scores preferably range from 0 to 1,000. The higher the score, thegreater the probable recovery. A score of 1,000 indicates a probablefull recovery, while a score of 0 is given when no recovery is expected.

To develop the scores, first each subrogation claim X_(I) is encoded todescribe characteristics of:

1. the claim, legally liable party and tortfeasor's insurer I, where:

-   -   X_(I) ε {I(ij)| attribute j=1, . . . , k for characteristic        i=1,2, . . . 1}    -   Y_(I) ε {I(ij)| risk factor m=1, . . . , z associated with the        specific debt}

2. the current economic environment A, A ε {a₁, a₂, a₃, . . . , a_(n})

3. and, the collection strategies B, B ε {b₁, b₂, b₃, . . . , b_(n})

Scores are then calculated using expected values associated with theencoding in the baseline subrogation operation and in the economicenvironment. More specifically, For every Claim X_(IAB),

The SUBROGATION COLLECTION SCORE (X_(IAB, Y) _(I))=10,000 E(P_(I) R_(I)N_(IA) O_(IB))IIY_(Im), for m=1, . . . , z

SCORE ε [0, . . . , 10,000], where

-   -   P_(I)=probability of a legally liable party with characteristics        I making any payment;    -   R_(I)=probable percentage of losses recovered from parties who        make any payment;    -   N_(IA)=adjustment reflecting the difference in expected        recoveries from base expectations under economic scenario A;    -   O_(IA)=adjustment reflecting the difference in expected        recoveries from base expectations under operational scenario B;        and,    -   Y_(IM)=risk factors associated with characteristics of the claim

Factors vary by type of claim. Looking at each in turn.

P₁: The dominant general model for the Probability of a Legally LiableParty with Characteristics I Making Any Payment is in the form:P _(I)=α_(0I)+α_(1I) [eφ/(1+eφ]φ=1n(p _(ij))=σ_(0ij)σ_(1ij)(0,1)+σ_(2ij)(0,1)+ . . . σ_(kij)(0,1)for X_(I) ε {I(ij)| attribute j=1, . . . , k for characteristic i=1,2, .. . 1}

Predictive parameters are estimated based on an assessment of historicrecovery patterns using: (1) a combination of parametric andnonparametric statistical techniques including but not limited tobinomial estimation, binomial regression, general loglinear, and logitloglinear analysis, and Ordinal Regression, and, (2) heuristics. In allcases, the resultant P_(I)ε (0,1]

R_(I): The dominant form for the Percentage of Loss Collected whenpayments are made is a piece-wise defined function. This is because thepredominant recovery is 100%. Parameters are developed from anassessment of historic recovery patterns using: (1) a combination ofparametric and nonparametric statistical techniques including but notlimited to: general loglinear, and logit loglinear analysis, and OrdinalRegression, (2) age-to-age analysis trends, and (3) heuristics in allcases: R_(I)ε (0,1]

N_(IA) Economic Scenarios reflect, but are not limited to,characteristics such as: (1) Unemployment and bankruptcy rates, (2)Interest rates and tight money conditions, (3) Financial strength oflegally liable party's geographic area and industry, (4) Inflation, and(5) Leading economic indicators. The dominant form is [1+E(ν_(Ia)] fora=1, . . . , n. where, ν_(Ia) is a binomial distribution≅N(0,1). Factorsare developed using monte carlo simulation techniques, based onheuristics and/or statistical observations of payor and liquidationpatterns.

O_(IB) Operational Scenarios reflect, but are not limited to,characteristics such as: (1) Type and amount of prior collectionefforts, (2) Quality of subrogation screening, investigation anddocumentation conducted during settlement of underlying claim, (3)Thresholds for attorney involvement, filing of law suits and use ofalternative dispute resolutions mechanisms, (4) Settlement authority andstrategies, and (5) Positions on skip tracing and additionalinvestigation efforts. The dominant form is [1+E(O_(Ib))] for b=1, . . ., n. where, O_(Ib) is a binomial distribution≅N(0,1). Factors aredeveloped using monte carlo simulation techniques, based on heuristicsand/or statistical observations of payor and liquidation patterns.

Y_(IM)=Risk Factors reflect, but are not limited to, characteristicssuch as: (1) Limitations of legal process due to statutes oflimitations, (2) Degree of Insured's and tortfeasor's legal culpabilityin specific claim, (3) Reduced collection potential evidenced by failureof other collection agencies to recover on claim, and (5) Limitationsdue to difficulty in identifying and/or locating tortfeasor. Thedominant form is [1+E(ω_(Im))] for m=1, . . . , z. where, ω_(Im) is abinomial distribution≅N(0,1). Factors are developed using monte carlosimulation techniques, based on heuristics and/or statisticalobservations of payor and liquidation patterns.

Validation of the mathematical models is done in several steps. Thefirst goodness-of-fit criteria to be met is a demonstrated unbiasedpattern of Chi-square residuals made against the original fitted data.This is followed by differing levels of stochastic-based retrospectivetesting as well as simulated user tests.

The NET BACK SCORE is a composite score incorporating the cost ofcollection into the predictions. It reflects the estimated relativeexpected loss recoveries net of expense (netback). Options are to do soat the end of 12, 15, 18, or 24 months in the future or to do so atultimate. As set forth, NET BACK SCORE=LIQUIDATION SCORE—EXPENSE SCORES,all at time t. The LIQUIDATION and SUBROGATION COLLECTION SCORES aresisters. They differ only in the predictive timeframe. When theLIQUIDATION SCORE is calculated at ultimate, the scores are twins.

The EXPENSE SCORE reflects either unit costs for specific types ofactivities or general expense loads. It is based on either specific orgeneralized operational strategies.

Like the COLLECTION SCORE, NET BACKS SCORES are derived from algorithmsfactoring in characteristics of the underlying insurance claim, legallyliable party, tortfeasor's insurer and the current economic environment.

Costs can reflect those of a standard subrogation facility or one usingcustomized operational strategies.

In one embodiment of the present invention, scores range from −10,000 to+10,000. The higher the score, the greater the probable recovery. Ascore of 10,000 indicates probable full recovery at no material expense.A score of 0 is given when collection costs are equal to recoveries. Ascore of −10,000 reflects expected expenses at least twice exceedexpected recoveries.

As set forth, to develop the scores, first each subrogation claim X_(I)is encoded to describe characteristics of the claim, legally liableparty, and tortfeasor's insurer I, where

X_(I)={I(ij)|j refers to attribute 1, . . . , k for each characteristici=1,2, . . . . }

Y_(I)={I(i,j)| risk factor m=1, . . . , z associated with the specificdebt}, the current economic environment A, A={a₁, a₂, a₃, . . . ,a_(n})collection strategies B. B={b₁, b₂, b₃, . . . , b_(n})unit costfactors C, C={c₁, c₂, c₃, . . . C_(n})

A score set is then developed for time T, where T={t1=12 months, t2=15months, t3=18 months, t4=24 months, t5=at ultimate}. Each NETBACK SCOREset is calculated using expected values associated with the encodingover the range of time t. For Claim. X_(IAB), and t=1, 2, 3, 4, 5,SUBROGATION NETBACK SCORE (X_(IABt), Y_(I))=LIQUIDATION SCORE (X_(IABt)Y_(I))−EXPENSE SCORE (X_(IABt)). LIQUIDATION SCORE (X_(IABt),Y_(I))=10,000 E(P_(It) R_(It)N_(IAt) O_(IBt)) for m=, . . . , z, limitedto be in [10.0000, . . . 10,000].

Calculating the LIQUIDATION SCORE is otherwise identical to theSUBROGATION COLLECTION SCORE. The LIQUIDATION SCORE is based onevaluation of recoveries over the range of time t; the SUBROGATIONSCORE, at ultimate.

The EXPENSE SCORE, however is unique. EXPENSE SCORE (X_(IABt))=10,000E(U_(IBt) O_(IBt)), where U_(IBt)=Unit Cost of Collection Activity underOperational Scenario B through time t. Q_(IBt)=Adjustment Reflecting theDifference in Expected Costs Under Operational Scenario B, From theBaseline. Looking at the mathematical factors in turn,

U_(IBt) Unit Costs under Operational Scenarios B reflect but are notlimited to, characteristics such as: (1) (Cost of phone calls)×(ExpectedNumber of calls to be made), (2) (Cost of letters mailed)×(ExpectedNumber of letters to be sent), (3) (Cost of additional investigation,legal documentation expense, and skip tracing)×(probability this will benecessary), and, (4) (Costs of legal action over the spectrum of legalactions possible)×(probability this will be necessary). The dominantform is [1+E(χ_(Im))] for c=1, . . . , n, where, χ_(Im) is a binomialdistribution≅N(0,1). Factors are developed using monte carlo simulationtechniques, based on heuristics and/or statistical observations.

Q_(IBt) These factors calibrate the scores to reflect operational costsappropriate for the target claims. The dominant form is [1+E(χIm)] forc=1, . . . , n. Factors will be provided by the operational managers inthe form of absolute or point estimates.

The validation process is identical to that of the SubrogationCollection Scores.

The NET LIQUIDATION SCORE is a more sophisticated version of the NETBACK SCORE. Values reflect the net present value of both the recoveriesand expenses, based on prevalent interest rates. This score is usefulfor subrogation portfolio purchases.

At an interest rate of 0, the NET LIQUIDATION SCORE and NET BACK SCOREat time ultimate are identical. The mathematics differ only inapplication of a present value factor to all components. Validationprocedures are identical.

Table-1 summarizes the foregoing as a chart of attributes and riskfactors:

TABLE 1 Risk Attributes Factors X underlying insurance Xpreferred/standard nonstandard/ affinity group X personal/commerciallines X membership of first and third party insurers in commonalternative dispute resolution organizations underlying insurance claimX number of collection agencies who have previously worked the account Xtype of underlying claim X size of claim X length of time since lossoccurrence when this collection effort is initiated X negligence lawsassociated with the state of legal jurisdiction X relative degree ofinsured's and tortfeasor's negligence X whether legal action wasinitiated under statute of limitation or whether there was someextension of statute of limitations X existence and type of legaljudgments rendered X legally liable party (tortfeasor) X Type ofinformation available to identify and locate responsible party X foruninsured/underinsured individual or group of individuals X incomelevels X homeownership X gender X presence of children X number of yearsat current residence X age X marital status X for uninsured businesses Xbankruptcy X existence of liens, judgments, derogatory legal informationX problems in paying suppliers X number of employees (size of business)X other financial strength indicators X length of time in business Xbusiness structure (incorporated, “doing business as”, LLC) X type ofbusiness X for other uninsured parties X nonprofit agency X fraternal Xgovernment agency X for tortfeasor's insurer X financial strength Xresistance to pay characteristics X common membership in alternativeresolution pools X type of insurance business

To use this information, a table of base scores is established using anhistoric assessment of collection patterns for subrogation claims withthe common attributes identified above. These represent the averagerecovery expectations for claims with similar attributes. This “average”base score is then fine tuned using the risk factors above to reflectthe specific recovery expectations for each individual claim.

EXAMPLE 1

For simplicity, an example is now discussed in which there are onlythese attributes: (1) Type of underlying claim—automobile physicaldamage or workers compensation; Size of, Claim—$500 or $1,000; and, Typeof Legally Liable Party —person or business. The base score table thenwould have 8 entries, each one reflecting the average historicexpectations for the type of claims. Exemplary data is provided inTable-2.

TABLE 2 CLAIM TYPE CLAIM SIZE PARTY TYPE SCORE AUTO PHYS DAMAGE $500PERSON 350 AUTO PHYS DAMAGE $1000 PERSON 750 AUTO PHYS DAMAGE $500BUSINESS 400 AUTO PHYS DAMAGE $1000 BUSINESS 800 WORKERS COMP $500PERSON 300 WORKERS COMP $1000 PERSON 600 WORKERS COMP $500 BUSINESS 350WORKERS COMP $1000 BUSINESS 650

Supposing “Party A” and “Party B” each had separate car accidents with$500 worth of damage. According to a preferred form of the presentinvention, you start with a base score of 350 reflecting a 35% chance ofloss recovery. The second step according to the present invention is toadjust the base for the set of claim specific risk factors Again forsake of explanation, it will be assumed there are only two: Statute oflimitations—too late to take legal action or plenty of time, and Degreeof negligence—other party either 100% or 50% to blame. These areindependent conditions and, like all risk factors, are assessedseparately. The final score becomes a product of the base score and allrisk factors. For example, the Final score=base score×risk factor1×riskfactor 2.

For the exemplary case the risk factors can be summarized as indicatedin Table 3.

TABLE 3 RISK FACTOR CLASS SUBCLASS RISK FACTOR STATUTE OF LIMIT Too lateto take action 0.0000 STATUTE OF LIMIT Plenty of time 1.0000 OTHERPARTY'S NEG 100% 500.0000 OTHER PARTY'S NEG 0.0% 1.0000

Assuming Party B was totally to blame, but Party A's insurer took toolong to pursue the case, the final score for Party A would be calculatedas: Party A base score (350)×other party to blame (500)×too late foraction (0.0000)=0.0000. This reflects a 0% chance of recovering lossesafter the statute of limitations has passed and there is no legalrecourse, which is reasonable score for this case.

For a second example, assume Partys A and B are equally to blame andthat there is plenty of time to file legal action. Party B's final scorecan be calculated as Party B base score (350)×other party 50% to blame(1.0000)×plenty of time (1.0000)=350. It should be noted there isnothing about this particular claim that would temper or override thebase score. Thus, the factors correctly leave the base score unchanged.This final score reflects a 35% chance of recovering losses, average forthis type of claim. Collections depend on the skill of the negotiatorsand willingness and ability of the other party to pay.

EXAMPLE 2

Table-4 summarizes information provided by a customer in a secondexample.

TABLE 4 Type of Claim Auto Collision Accident Description IV & OVcollided in X Claim Handler Negligence Assessment None availableAccident State California Loss to be recovered $1,250 Deductible to berecovered $250 Type of insurance from which claim arose StandardPersonal Auto Policy Age of Claim at time placed with 65 dayssubrogation unit Legal Action Started? Judgment Rendered? No LegallyLiable Party Person Information Available to identify party Name,address, phone Insurance Status Uninsured Number of collection agenciesworking 1 account

This information can be supplemented by information about the legallyliable party which can be obtained through automatically generatedqueries of commercially available databases for example. Table 5summarizes exemplary information that can be obtained.

TABLE 5 Gender Male Homeownership Probable Years at Current Address 2Household Income 45,000 Age 23I. Subrogation Identification Screening Process

An analytical approach is used to screen for subrogation potential. Itqueries the information provided, assessing whether it has sufficientinformation to determine subrogation potential or additional informationis required. It is dynamic with a learning—feedback mechanism, thatcontinuously identifies new vocabulary and phrases. Once encountered,new phrases and related risk assessments are added to the utilizedsubrogation encyclopedia. Statutory rule changes are also madeperiodically. As an option, answers may be calibrated for specificcustomers.

More particularly, in a preferred embodiment the screening process canbe summarized as follows:

Is this a 1st party claim which insurers typically have the right tosubrogate? Yes. Standard Personal Auto contracts typically containcontractual language giving the insurer the right to recover paymentsmade to indemnify the insured when someone else is legally liable forall or part of the damages.

Are there legal prohibitions against subrogation in the particular stateof legal jurisdiction? There are none in California.

Is the claim closed or expected to close without payments? No. Recoveryof $1,500 is being sought.

Have rights under the statute of limitation been preserved? Is theretime to bring legal action? The loss event occurred 65 days ago, leavingplenty of time to begin legal action within the statute of limitation.

Does the accident description of other information provided show someoneelse might be liable for all or part of the loss? Yes. We areinterpreting “IV & OV collided in X” to mean the insured vehicle andanother vehicle collided in an intersection. The assessment is thatnegligence is shared, probably 50% /50% between the insured and theother party.

Does the state bar recovery based on the insured's degree of negligence?No. California is a “pure” comparative negligence state. Insurer wouldbe entitled to 50% recovery based on a shared 50% negligence.

Is there enough information to identify and locate the alleged legallyliable party? There appears to be. Full name address and phone numberwere provided.

Using convention business logic, this analysis leads to a conclusionthere is “DEFINITE SUBROGATION POTENTIAL” and that a “SUBROGATIONRECOVERY POTENTIAL CAN BE SCORED” for example.

II. Calculation of a Subrogation Collection Score

A mathematical approach to calculate a score is used. This process isalso dynamic, incorporating a feedback loop. Actual results are comparedwith statistical expectations for benchmark books at various points intime. Whenever a pattern develops where the scores vary beyondstatistical expectations, the scores are recalibrated. In a preferredembodiment, the analytical algorithm is as follows:

For every claim X_(IAB), the COLLECTION SCORE (X_(IAB), Y_(I))=10,000E(P_(I)R_(I)N_(IA)O_(IB)) IIY_(IM), where

P_(I)=Probability of a legally Liable Party with Characteristics IMaking Any Payment

R_(I)=Probable Percentage of Losses Recovered from parties who Make AnyPayment

N_(IA)=Adjustment Reflecting the Difference in Expected Recoveries fromBase Expectations Under Economic Scenario A

Q_(IB)=Adjustment Reflecting the Difference in Expected Recoveries fromBase Expectations Under Operational Scenario B; and,

Y_(IM)=Risk Factors associated with characteristics of the claim.

Let I be the claim presented above where we are seeking recovery of$1,500 arising from a California personal auto collision claim, examined65 days after the accident. We are subrogating against an uninsuredindividual for an accident described as “IV & OV collided in X”. Thecomplete name and address is provided for the driver of the othervehicle. This is the 1^(st) agency trying to recover the money.

STEP 1. Calculate P_(I), the Probability of a Legally Liable PartyMaking a Payment.

P_(I)=P(ij) where the “i” factor reflects a personal lines collisionclaim with an uninsured individual as legally liable party. The “j”factor reflects characteristics of the claim and legally liable party.

For auto collision, P_(I) takes the form of α₀₁+α₁₁[e^(θ)/(1+e^(θ))]θ=1n(p_(ij)/(1+p_(ij)))=σ_(0i)+σ_(1ij)(0,1)+σ_(2ij)(0,1)+σ_(3ij)(0,1)+σ_(4ij)(0,1)+σ_(5ij)(0,1)+σ_(6ij)(0,1)+σ_(7ij)(0,1)+σ_(8ij)(0,1)For this claim i . . .

σ₀ is the logistic regression constant.

σ_(0i)=0.829

σ_(1ij)(0,1) refers to the size of claim.

$\begin{matrix}{{\sigma_{1{i{({{{\$ 1}\text{,}001} - {2\text{,}000}})}}}( {0,1} )} = {{1.009*0} + {{.2570}*0} - {{.019}*1} - {{.038}*0} -}} \\{{{.1620}*0} - {{.2860}*0} - {{.2860}*0} - {0.2855*}} \\{0 - {0.285*0} - {0.2845*0} - {0.284*0}} \\{= {- 0.019}}\end{matrix}$

σ_(2ij)(0,1) refers to the age of claim.

$\begin{matrix}{{\sigma_{2{i{({61 - {90\mspace{11mu}{days}}})}}}( {0,1} )} = {{{- 1.214}*0} - {1.4540*1} - {2.018*0} -}} \\{{2.4110*0} - {2.678*0} - {2.247*0} - {1.851*}} \\{0 + {{- 1.846}*0}} \\{= {- 1.454}}\end{matrix}$

σ_(3ij)(0,1) refers to the accident state negligence laws.

$\begin{matrix}{{\sigma_{3{i{({{comparitive}\mspace{14mu}{negligence}})}}}( {0,1} )} = {{1.3055*0} - {{.04}*1} - {{.2365}*0} -}} \\{{{.2365}*0} - {1.3055*0} - {1.99*0} -} \\{{.8523}*0} \\{= {- {.04}}}\end{matrix}$

σ_(4ij)(0,1) refers to the age of the legally liable party.

$\begin{matrix}{{\sigma_{4{i{({19 - {35\mspace{11mu}{years}\mspace{14mu}{old}}})}}}( {0,1} )} = {{{.472}*1} - {{.38}*0} - {{.722}*0} - {{.832}*}}} \\{0 - {0.3655*0} + {0.472*0}} \\{= {.472}}\end{matrix}$

σ_(5ij)(0,1) refers to household income.

$\begin{matrix}{{\sigma_{5{i{({{{\$ 35}\text{,}000} - {49\text{,}999}})}}}( {0,1} )} = {{{- 1.3055}*0} - {{.04}*1} - {{.2365}*0} - {{.2365}*}}} \\{0 - {1.3055*0} - {1.99*0} - {0.8523*0}} \\{= {- {.04}}}\end{matrix}$

σ_(6ij)(0,1) refers to gender.

$\begin{matrix}{{\sigma_{6{i{({male})}}}( {0,1} )} = {{{- {.45}}*0} - {{.62}*1} - {{.0}*0} - {{.86}*0}}} \\{= {- {.62}}}\end{matrix}$

σ_(7ij)(0,1) refers to homeownership.

$\begin{matrix}{{\sigma_{7{i{({{probable}\mspace{14mu}{homeowner}})}}}( {0,1} )} = {{{- {.171}}*1} - {{.231}*0}}} \\{= {- {.171}}}\end{matrix}$

σ_(8ij)(0,1) refers to the number of years at the current address.

$\begin{matrix}{{\sigma_{8{i{({1 - {3\mspace{14mu}{years}}})}}}( {0,1} )} = {{{.239}*0} + {{.778}*1} + {1.546*0} + {4.783*}}} \\{0 + {1.3736*0}} \\{= {.778}}\end{matrix}$For this claim,θ=0.829−0.019−1.454″0.04+0.474−0.04−0.62−0.171+0.778e⁷⁴=0.76874It should be noted there were no calibration adjustments for this claim

α_(0I), the additive calibration factor=0

α_(1I), the multiplicative calibration factor=1

Putting the pieces together, the probability of the legally liable partyfor this claim making a payment, the expected value.

$\begin{matrix}{E( {P_{I)} = {\alpha_{0I} + {\alpha_{1I}\lbrack {{\mathbb{e}}^{\theta}/( {1 + {\mathbb{e}}^{\theta}} )} \rbrack}}} } \\{= {0 + {1( {{.76874}/( {1 + {.76874}} )} }}} \\{= {43.46\%}}\end{matrix}$STEP 2. Next, calculate R_(I)=the Probable Percentage of LossesRecovered from Parties who Make Any Payment.

I=R(ij). The “i” factor reflects a personal lines collision claim withan uninsured individual as legally liable party, the “j” factor reflectsthe size of claim ($1,001-$2,000).

or this claim, The expected value, E(R(I, $1,001-$2,000))=0.7485

STEP 3. Through N_(IA), factor in any adjustments needed for differencesfrom the baseline due to changes in economic conditions.

N_(IA)=N(i,a)=1+E(v_(ia)). The “i” factor reflects a personal linescollision claim with an uninsured individual as legally liable party.The “a” factor reflects the economic environment of the legally liableparty's residence in California, plus countywide inflation factors andchanges in the leading economic indications.

For this claim, the expected value E(N_(IA))=1+(0.026)=1.026

STEP 4. O_(IB)=adjusts for differences from the baseline due todifferences in operation strategies or efficiencies.

O_(IB)=O(i,b)=1+E(O_(ib)). The “i” factor reflects a personal linescollision claim with an uninsured individual as legally liable party,the “b” factor reflects known differences in collection strategies thatwould impact these types of recoveries.

For this claim we have factored in no differences from our benchmark.

The expected value E(O_(ib))=1 +(0.0)=1.000.

In STEP 5 we can now put together the major piece of our equation. Forthis claim, E(P_(I)R_(I)N_(IA)O_(IB))=43.46%×0.7485 X(1.026)×1.000=33.38% This is the expected recovery rate for this type ofclaim.

IN STEP 6, through Y_(IM) specific risk factors associated with thecircumstances of the claim are brought in.

m_(i1), modifies recovery expectations due to limitations of legalprocess arising from any statutes of limitations or other stateprohibitions. For this claim, m_(i1)=1.0000. Working within the statueof limitations should not present a problem and there are no stateissues.

m_(i2), modifies recovery expectations due to state recovery limitationsbased on the insureds culpability. For this claim, the insured and otherdriver were assessed as both 50% at fault. This is the averageexpectation in our baseline and no adjustments were made. Herem_(i2)=1.0000.

m_(i3), modifies expectations if other agencies have attempted andfailed to recover for the claim. Since this is indicated to be the 1stsubrogation placement, no modifications were made. m_(i3)=1.0000

m_(i4), modifies expectations due to difficulty in identifying orlocating a legally liable party. Since sufficient information appears tobe available, no adjustments were needed. m_(i4)=1.0000.

For this claim IIY_(IM)=1.000×1.000×1.000×1.000=1.000

STEP 7 completes the calculations for this claim

The COLLECTION SCORE (X_(IAB), Y_(I))=10,000 E(P_(I)R_(I)N_(IA)O_(IB))IIY_(IM)=10,000×33.38%×1.000=3,338

III. Calculation of a Netback Score

The netback score is a sister of the collection score, building on itsmathematical algorithms. It differs in two areas.

-   -   1. Recovery potential is quantified at the end of specific        period(s) of time working the debt.    -   2. Collection expense is factored in.

The calculation is illustrated using the same example, incorporating twoadditional pieces of information. We are interested in the score basedon 18 months of collections and factor in a 20% contingent collectionfee.

The analytical algorithm, for every claim X_(IAB), can preferably becharacterized as follows:SUBROGATION NETBACK SCORE (X _(IAB) , Y _(I))=LIQUIDATION SCORE (X_(IABt) , Y _(It))−EXPENSE SCORE (X _(IABt) , Y _(It))STEP 1—LIQUIDATION SCORE: The mathematical algorithms for theliquidation and subrogation score are identical. The difference is thatparameter values selected reflect the time series points. Withoutrepeating the details, for our example:

LIQUIDATION  SCORE  (X_(IAB(18 months)), Y_(I)) $\begin{matrix}{= {10\text{,}000\;{E\lbrack {P_{I{({18\mspace{11mu}{months}})}}R_{I{({18\mspace{11mu}{months}})}}N_{{IA}{({18\mspace{11mu}{months}})}}O_{{IB}{({18\mspace{11mu}{months}})}}} \rbrack}\Pi\; Y_{IM}}} \\{= {10\text{,}000\mspace{11mu} X\mspace{11mu} 29.8\%\mspace{11mu} X\mspace{11mu} 1.000}} \\{= {2\text{,}980}}\end{matrix}$Note, that the lower score reflects the omission of :collections madeafter 18 months.STEP 2—EXPENSE SCORE: The expense score factors into the equationcollection costs through time frames corresponding to the companionliquidation score.EXPENSE SCORE (X _(IABt) , Y _(It))=10,000 E(U _(IBt) O _(IBt))

Where U_(IBt)=Cost of Collection under Operational Scenario B throughtime t

For our example, cost is a function of the liquidation score

$\begin{matrix} {U_{{Ib}{({18\mspace{11mu}{months}})}} = {\Gamma\{ {{E\lbrack {P_{I{({18{mo}})}}R_{I{({18{mo}})}}N_{{IA}({18{mo}}}} )}O_{{IB}{({18{mo}})}}} \rbrack\Pi\; Y_{IM}}} \} \\{ {= {20\%\;{E\lbrack {P_{I{({18\mspace{11mu}{months}})}}R_{I{({18\mspace{11mu}{months}})}}N_{{IA}({18\mspace{11mu}{months}}}} )}O_{{IB}{({18\mspace{11mu}{months}})}}}} \rbrack\Pi\; Y_{IM}} \\{= {20\% X\; 29.8\%}} \\{= {5.98\%}}\end{matrix}$

O_(IBt)=is a calibration factor used to adjust the cost estimates tocurrent.

None was needed for our example and the baseline factor is 1.000.

Putting the pieces together for our example.

$\begin{matrix}{{{EXPENSE}\mspace{14mu}{SCORE}\mspace{14mu}( {X_{IABt},Y_{It}} )} = {10\text{,}000\mspace{11mu}{E( {U_{IBt}O_{IBt}} )}}} \\{= {10\text{,}000\mspace{11mu} X\; U_{Ibt}X\; U_{Ibt}}} \\{= {{10\text{,}000\; X\; 5.98\%\; X\; 1.000} = 596}}\end{matrix}$STEP 3—Completing the calculations:

$\begin{matrix}{{{NETBACK}\mspace{14mu}{SCORE}} = {{{LIQUIDATION}\mspace{11mu}{SCORE}\mspace{11mu}( {X_{IABt},Y_{It}} )} -}} \\{{EXPENSE}\mspace{14mu}{SCORE}\mspace{14mu}( {X_{IABt},Y_{It}} )} \\{= {{{2\text{,}980} - 596} = {2\text{,}384}}}\end{matrix}$IV. Calculations of a Net Liquidation Score

The NET LIQUIDATION SCORE is the discount value of the NetBack Scorewhere accounts are worked to ultimate completion. Mathematically, forevery claim X_(IAB),

SUBROGATION  NET  LIQUIDATION  SCORE  (X_(IAB), Y_(I)) = 10,000  present  value  [.0001  X  LIQUIDATION  SCORE  (X_(IAB), Y_(I)) − .0001 X  EXPENSE  SCORE  (X_(IAB), Y_(I))For our example above calculations for the liquidation score and expensescore are identical to those already explained. To complete the score:

NET  LIQUIDATION  SCORE  (X_(IAB), Y_(I)) = 10, 000  X  present  value$\begin{matrix}{\lbrack {{{.0001}X\; 3{Null}{Null}338} - {\;{{.0001}\; X\; 668}}} \rbrack = {10\text{,}000\mspace{11mu} X\mspace{11mu}{present}\mspace{14mu}{value}\mspace{14mu}( {26.7\%} )}} \\{= {{10\text{,}000\mspace{11mu} X\mspace{11mu} 23.3\%} = {2\text{,}332}}}\end{matrix}$

Although the invention has been described and pictured in a preferredform with a certain degree of particularity, it is understood that thepresent disclosure of the preferred form, has been made only by way ofexample, and that numerous changes in the details of construction andcombination and arrangement of parts may be made without departing fromthe spirit and scope of the invention as hereinafter claimed. It isintended that the patent shall cover by suitable expression in theappended claims, whatever features of patentable novelty exist in theinvention disclosed.

We claim:
 1. A computer-implemented method for identifying select onesof insurance records which possess a favorable subrogation potential foran insurer, the method comprising: receiving by a processor dataindicative of a plurality of insurance claims as to which the insurerhas a right of subrogation, the data including, for each insuranceclaim, a type of insurance claim, a size of the insurance claim, a typeof responsible person, and whether the policy under which the claim ismade is a personal or business policy; calculating by the processor abase score for each of the claims indicative of likelihood of expectedsubrogation recovery dependently upon the received data, the calculatingof the base score employing attributes including: the type of insuranceclaim, the insurance claim type values comprising automobile physicaldamage, automobile collision, and workers compensation, the size rangeof an underlying insurance claim, and a type of responsible person, theresponsible person type values comprising individual or business;receiving by the processor risk factors for each of the claims,including whether a policy under which each claim was made is a personalor a business insurance policy, degree of insured's and tortfeasor'sculpability for subrogation recovery, reduced collection potentialevidenced by failure of other collection agencies to recover, andlimitations due to difficulty in identifying and/or locating tortfeasor;and, scoring by the processor each of the claims dependently upon thebase scores and received risk factors to provide a value indicative ofan expected subrogation recovery, the value not representing a sumcertain of an obligation to the insurer, and storing the value by theprocessor in a memory.
 2. The method of claim 1, further comprisingproviding by the processor recovery strategy recommendation and recoveryspecialist checklists to optimize steps taken to recovery losses atminimum expense.
 3. The method of claim 1, wherein the receiving thedata comprises: receiving the data in electronic form.
 4. The method ofclaim 1, wherein the receiving the data comprises: providing a userinterface; and, extracting the data from the user interface.
 5. Themethod of claim 1, wherein the calculating the base score comprisescalculating a likelihood a payment will be made by a responsible party.6. The method of claim 5, wherein the calculating a base score furthercomprises calculating a probable percentage of losses recovered throughpayments received from said responsible party.
 7. The method of claim 6,wherein the calculating a base score further comprises: identifying atleast one economic factor pertinent to said base score; and, calculatinga first adjustment dependently upon said identified at least oneeconomic factor.
 8. The method of claim 7, wherein the calculating abase score further comprises: identifying at least one collectionefficiency or strategy pertinent to said base score; and, calculating asecond adjustment dependently upon said identified at least onecollection efficiency or strategy.
 9. The method of claim 8, wherein thecalculating a base score further comprises calculating said base scoreusing said calculated likelihood a payment will be made, calculatedprobable percentage of losses recovered, calculated first adjustment andcalculated second adjustment.
 10. The method of claim 1, wherein saidrisk factors are identified using additional data from at least oneexternal database.
 11. The method of claim 1, wherein said risk factorsaddress recovery expectations due to time limitations.
 12. The method ofclaim 1, wherein said risk factors address recovery expectations due torecovery limitations based on a degree of fault assessed to saidresponsible party.
 13. The method of claim 1, wherein said risk factorsaddress if other agencies have attempted and failed to recover on theclaim, and said agencies are selected to include attorneys, inhouseefforts or outside agents.
 14. The method of claim 1, wherein said riskfactors address expected difficulties in locating said responsibleparty.
 15. The method of claim 1, wherein said risk factors addressissues selected from the group consisting of: expectations due to timelimitations, recovery expectations due to recovery limitations based ona degree of fault assessed to said responsible party, if other agencieshave attempted and failed to recover on the claim, and expecteddifficulties in locating said responsible party.
 16. The method of claim1, further comprising quantifying said value indicative of an expectedsubrogation recovery at specific periods of time.
 17. The method ofclaim 16, wherein said quantified values factor in expected collectionexpenses.
 18. The method of claim 16, wherein said quantifying atspecific periods of time comprises: calculating a liquidation value forsaid claim for each specified period of time; calculating an expectedexpense value for said claim for each specified period of time; and,calculating an quantified value for each specified period of time usingsaid calculated liquidation and expected expense values.
 19. The methodof claim 18, further comprising discounting said quantified values toprovide net liquidation values for each specified time period.
 20. Themethod of claim 1, wherein the calculating said base score comprises:automatically calculating an expected probability a responsible partywill make a payment; automatically calculating an expected probablepercentage of losses recovered through payments received fromresponsible parties; and, automatically adjusting resultant scores fordifferences due to economic conditions or operation strategies orefficiencies.
 21. A computerized system for identifying select ones ofinsurance records which possess a favorable subrogation potential, thesystem comprising: at least one computing device for receiving dataindicative of a plurality of insurance claims as to which an insurer hasa right to subrogation; and, a computer readable medium being accessibleto said computing device, said computer readable medium comprising: asequence of directions for automatically calculating a base score forthe claims indicative of a probability of expected subrogation recoverydependently upon the received data using said at least one computingdevice, the calculating of the base score employing attributes includinga type of underlying insurance claim the insurance claim type valuescomprising automobile physical damage, automobile collision, and workerscompensation, a size of an underlying insurance claim, and a type ofresponsible party, the responsible person type values comprisingindividual or business; a sequence of directions for automaticallyidentifying risk factors, including whether a policy under which eachclaim was made is a personal or a business insurance policy, degree ofinsured's and tortfeasor's culpability for subrogation recovery, reducedcollection potential evidenced by failure of other collection agenciesto recover, and limitations due to difficulty in identifying and/orlocating tortfeasor, for each of the claims using said at least onecomputing device; and, a sequence of directions for automaticallyscoring each of the claims dependently upon the base scores andidentified risk factors to provide a value indicative of an expectedsubrogation recovery, the value not representing a sum certain of anobligation to the insurer, using said at least one computing device.